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Study: About 1% of tech entrepreneurs who got venture capital in 2010 were black

Some people wonder whether "pattern matching" is hindering racial diversity

Investors use pattern matching to study the traits that helped past ventures succeed

Wayne Sutton has been asking venture-capital investors and Silicon Valley executives a question that's not often broached here in the epicenter of the technology industry:

"Why aren't there more black people in tech?"

The vast majority of top executives at the leading Silicon Valley tech firms are white men. Women and Asians have made some inroads, but African-American and Latino tech leaders remain a rarity. About 1% of entrepreneurs who received venture capital in the first half of last year are black, according to a study by research firm CB Insights.

This lack of diversity in Silicon Valley made headlines last month when influential tech blogger Michael Arrington, in an interview for CNN's upcoming documentary "Black in America: The New Promised Land: Silicon Valley," said, "I don't know a single black entrepreneur." Arrington later recanted the statement, saying he was caught off guard by the question, but the sensitive issue sparked a public dispute between the newly minted venture capitalist and CNN's Soledad O'Brien.

It's an issue that Sutton, who co-founded the NewMe Accelerator for under-represented minorities in the tech industry and is also building a software company, has been grappling with for months.

NewMe co-founder Wayne Sutton, center, works on his laptop at a coffee shop with fellow entrepeneurs.

NewMe is an incubator program formed to help minorities launch Internet ventures. For two months last summer, Sutton and seven other black entrepreneurs worked together in a rented house in Mountain View, California, where they got advice from successful executives and pitched their startup ideas to investors.

The venture capitalists, including business-software designer Mitch Kapor, told them the struggles of blacks in the tech industry might be attributed to a concept called "pattern matching," which is prevalent in venture-capital circles and yet alien to the rest of the business world.

"Silicon Valley really likes to think of itself as a meritocracy," Kapor said. In fact, "the general state of Silicon Valley is completely backwards," he said.

A true meritocracy?

Pattern matching comes from a computer-science exercise in which a system looks for common attributes within reams of data. Its companion is pattern recognition, a term that some investors use interchangeably but which describes a less precise method.

In the business of investing, pattern matching defines a technique for figuring out which human traits, corporate makeup and financial projections are the foundations for the next Facebook or other big Internet success. The criteria can include a founder's track record, personality type and alma mater, which market the company is targeting and how its peers are performing, and how quickly the business is expected to grow and begin collecting revenue.

Many of the top venture-capital firms use some form of pattern matching, but no two use precisely the same data sets. The firms typically don't disclose what exactly goes into their formulas because they see their patterns as trade secrets.

But Sutton's NewMe co-founder, Angela Benton, wonders whether pattern matching, which critics say favors the status quo over change, is a veiled form of racism.

"I was offended by it," said Benton, who is also starting a company, in an interview with CNN. "I'm black. I'm a woman. I have kids. I might as well go home."

People familiar with the pattern-matching process say race is not explicitly a factor, nor is gender.

"Most VCs I know pride themselves on the idea that to be a good investor, you have to learn those skills," said Cindy Padnos, who founded a venture capital firm called Illuminate Ventures. Her company says in its creed, "We don't rely on 'pattern recognition.'"

"I have no doubt that most of what we see happening in the high-tech community is completely unintentional bias, and yet, we all have to recognize (that) unintentional or not, we are all born with it," Padnos said. "Undoubtedly, the unintentional bias comes into play when they look at the 15 (startups) they did (invest in) and the five that succeeded big-time, when the ones that succeeded were led by white males. That somehow seeps into the equation."

These methods don't necessarily explain why so few blacks, Latinos and women lead tech companies when compared to the number of white-male execs.

Padnos and her venture firm have researched and advocated for hiring and funding more tech companies led by women. She says she has strived to diversify her 40-person advisory committee and her relatively small investment portfolio, but neither has an African-American.

High-placed female execs like Google's Marissa Mayer and Facebook's Sheryl Sandberg have advocated on their own for women, and women continue to break into the top executive ranks. Their recent successes, and the rise of Chinese and Indian tech leaders, could provide a model for other minorities in the United States who are hitting roadblocks in their quests to start Internet companies, industry experts say.

But their hard-earned victories have not come easily.

"We live in a society where race is still a factor," said Rey Ramsey, an African-American man who runs a political lobbying firm for the tech industry called TechNet. Referencing Facebook's famous young founder, he added, "We're waiting for the black Zuckerberg."

A few standout black leaders have emerged in tech over the years, said Charles Moore, an African-American who founded Rocket Lawyer, a startup that offers online legal documents and advice. He cited Frank Greene, a pioneering semiconductor engineer turned venture capitalist who died in 2009, and Charles Phillips Jr., the former Oracle co-president, as inspirations.

"Race has not really been a factor in a material way in my career," said Moore, whose company is backed by Google Ventures, the search giant's venture capital arm. "I think that Silicon Valley is a place where you can work hard and be smart and have some luck, which everybody needs. And if you have those things, you can succeed, regardless of your race or gender or other demographic profile."

Data-driven investing

Google Ventures has extolled the virtues of its data-driven approach to investing, in which it draws patterns from past home runs. For example, the firm has learned, as it told the New York Times in July, that successful entrepreneurs are more likely to create the spark again and that startups located far from the offices of their investors are more likely to succeed.

"Traditionally, the venture business is highly driven by qualitative assessments," said Bill Maris, Google Ventures' managing partner, recently at a conference. "We're trying to also look quantitatively, and apply some data tools and metrics around some questions that make natural sense to us to try and look at."

A longtime venture capitalist, who declined to speak on the record, said pattern matching, when applied correctly, is designed to spot the rare talent that may hit it big, not promote sameness. It has nothing to do with race, he said.

Many of the top venture firms have said publicly that they use pattern matching to identify potentially successful startups. But when contacted by CNN, they were reluctant to talk about it. Spokespeople for several high-profile venture firms, including Andreessen Horowitz and Sequoia Capital, did not return requests for comment on this story.

Ben Horowitz, who founded his venture firm with Marc Andreessen, wrote on his blog last year that he understands the value of pattern matching, which allows investors to "recognize patterns of strategy and behavior that generally work, and patterns that generally fail," although he thinks it has limits.

Sequoia Capital's Michael Moritz has said that age is a factor his firm considers when deciding who to invest in.

New Venture Partners, a New Jersey-based firm with an office in Silicon Valley, uses pattern matching, said partner Daniel Deeney in an interview. But race is not a factor, he said, and several companies New Venture has invested in have black leaders. He said New Venture's investments tend to go into the telecom industry, where AT&T, Lucent and others have fostered corporate diversity programs that have fostered minority-led startups.

A racial divide with no clear explanation

Little research has been done on the topic of race in the tech industry. Some observers, including Ramsey, wonder whether the lack of racial diversity can be blamed on hiring practices at tech juggernauts such as Apple, Facebook and Google, none of which cooperated with a request by CNNMoney for employee diversity statistics.

"The tech industry is pretty clubby," said Hank Williams, an African-American entrepreneur in the NewMe program who had success in the Internet boom of the 1990s. "There are really no people of color in Silicon Valley."

Others say the issue could be rooted deep within the black community. The NewMe co-founders said African-American families don't typically encourage business leaders or programmers to pursue interests in tech.

"The African-American community is like, 'Oh, nobody is going to give you money to make a website,'" Sutton said. "It's almost like stupid, or not cool."

Still others, like the founder of Kapor Capital, say risk-aversion within venture capital stifles changes in the demographics of entrepreneurs chosen to be financed.

"African-American candidates are much more likely not to match the pattern," said Kapor, who hosted NewMe's demo day at his office. "To recognize the truth is to accept that the winners at the top did so through a rigged game."

His wife, Freada Kapor Klein, a longtime adviser to tech firms on racial diversity in employment, believes people of different racial or ethnic backgrounds can solve problems with technology that others can't because of perspectives learned during their upbringings. Firms that recognize this, she said, should have an edge on less open-minded rivals.